Simoff / Böhlen / Mazeika Visual Data Mining

Theory, Techniques and Tools for Visual Analytics
Erscheinungsjahr 2008
ISBN: 978-3-540-71080-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 4404, 407 Seiten, eBook

Reihe: Lecture Notes in Computer Science

The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected.This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.

Zielgruppe

Research

Autoren/Hrsg.

Simoff, Simeon

Böhlen, Michael H.

Mazeika, Arturas

Inhaltsverzeichnis

Visual Data Mining: An Introduction and Overview.- Visual Data Mining: An Introduction and Overview.- 1 – Theory and Methodologies.- The 3DVDM Approach: A Case Study with Clickstream Data.- Form-Semantics-Function – A Framework for Designing Visual Data Representations for Visual Data Mining.- A Methodology for Exploring Association Models.- Visual Exploration of Frequent Itemsets and Association Rules.- Visual Analytics: Scope and Challenges.- 2 – Techniques.- Using Nested Surfaces for Visual Detection of Structures in Databases.- Visual Mining of Association Rules.- Interactive Decision Tree Construction for Interval and Taxonomical Data.- Visual Methods for Examining SVM Classifiers.- Text Visualization for Visual Text Analytics.- Visual Discovery of Network Patterns of Interaction between Attributes.- Mining Patterns for Visual Interpretation in a Multiple-Views Environment.- Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships.- Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data.- Context Visualization for Visual Data Mining.- Assisting Human Cognition in Visual Data Mining.- 3 – Tools and Applications.- Immersive Visual Data Mining: The 3DVDM Approach.- DataJewel: Integrating Visualization with Temporal Data Mining.- A Visual Data Mining Environment.- Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia.- Towards Effective Visual Data Mining with Cooperative Approaches.